Point-wise models such as PointNet [1]} and PointNet++ [2]} directly operates on point clouds. The former used MLPs to extract point-wise features and permutation- invariant max pooling operation to obtain a global feature. The latter built a hierarchical architecture that incorporates point downsampling and local structure aggregation strategies. Inspired by PointNet [1]} and PointNet++ [2]}, many recent works propose advanced local feature learning modules. For example, PointWeb [5]} built a dense fully connected web to explore local context, and used an Adaptive Feature Adjustment module for feature refinement. GACNet [6]} proposed to selectively learn distinctive features by dynamically assigning attention weights to neighbouring points based on spatial positions and feature differences.
ShellNet [7]} built a model with several layers of ShellConv, and solved point ambiguity by constructing concentric shells and applying 1D convolution on ordered shells.
Derived from point-based methods, some recent works define explicit kernels for point convolution. KCNet [8]} developed a kernel correlation layer to compute affinities between each point’s K nearest neighbors and a predefined set of kernel points. Local features are acquired by graph pooling layers. SpiderCNN [9]} designed a family of Taylor polynomial kernels to aggregate neighbor features. PointCNN [10]} introduced \(\chi \) -transformation to exploit the canonical order of points. PCNN [11]} built a network using parametric continuous convolutional layers. SPH3D [12]} used spherical harmonic kernels during convolution on quantized space to identify distinctive geometric features. KPConv [13]} defined rigid and deformable kernel points for local geometry encoding based on the Euclidean space relations between kernel point and neighborhood supporting points.
